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Fix GROOT N1.7 relative action stats

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This commit is contained in:
Andy Wrenn
2026-06-20 19:08:11 -07:00
parent 55da4bf8aa
commit ac828bfbee
2 changed files with 451 additions and 24 deletions
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tests/policies/groot/test_groot_n1_7.py
+98 -15
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@@ -30,7 +30,6 @@ from lerobot.configs import FeatureType, PolicyFeature
from lerobot.policies.factory import make_policy_config, make_pre_post_processors
from lerobot.policies.groot.configuration_groot import (
GROOT_ACTION_DECODE_TRANSFORM_LIBERO,
GROOT_N1_7,
GROOT_N1_7_BASE_MODEL,
GrootConfig,
infer_groot_n1_7_action_execution_horizon,
@@ -370,7 +369,7 @@ def test_groot_n1_7_accepts_named_action_decode_transform():
def test_groot_n1_7_rejects_legacy_libero_gripper_action_decode_transform(legacy_transform):
with pytest.raises(ValueError, match="Unsupported GR00T N1.7 action decode transform"):
GrootConfig(
action_decode_transform=legacy_transform,
action_decode_transform=legacy_transform,
device="cpu",
)
@@ -378,7 +377,7 @@ def test_groot_n1_7_rejects_legacy_libero_gripper_action_decode_transform(legacy
def test_groot_config_rejects_mismatched_n1_5_path_for_n1_7():
with pytest.raises(ValueError, match="does not match base_model_path"):
GrootConfig(
base_model_path="nvidia/GR00T-N1.5-3B",
base_model_path="nvidia/GR00T-N1.5-3B",
device="cpu",
)
@@ -504,7 +503,7 @@ def test_groot_from_pretrained_rejects_mismatched_caller_config(tmp_path):
# so construction itself raises before from_pretrained is reached.
with pytest.raises(ValueError, match="does not match base_model_path"):
config = GrootConfig(
base_model_path="nvidia/GR00T-N1.5-3B",
base_model_path="nvidia/GR00T-N1.5-3B",
input_features=input_features,
output_features=output_features,
device="cpu",
@@ -1004,6 +1003,77 @@ def test_groot_n1_7_pack_inputs_normalizes_action_chunk_per_dimension_before_pad
assert action_mask[0, :, 3:].sum().item() == 0
def test_groot_n1_7_pack_inputs_trains_native_relative_groups_with_absolute_gripper():
step = GrootN17PackInputsStep(
action_horizon=2,
valid_action_horizon=2,
max_state_dim=6,
max_action_dim=6,
normalize_min_max=True,
clip_outliers=False,
stats={
OBS_STATE: {
"min": [-100.0, -100.0, -100.0, -100.0, -100.0, 0.0],
"max": [100.0, 100.0, 100.0, 100.0, 100.0, 100.0],
},
ACTION: {
"min": [-10.0, -10.0, -10.0, -10.0, -10.0, 0.0],
"max": [10.0, 10.0, 10.0, 10.0, 10.0, 100.0],
},
},
raw_stats={
"state": {
"single_arm": {"min": [-100.0] * 5, "max": [100.0] * 5},
"gripper": {"min": [0.0], "max": [100.0]},
},
"action": {
"single_arm": {"min": [-100.0] * 5, "max": [100.0] * 5},
"gripper": {"min": [0.0], "max": [100.0]},
},
"relative_action": {
"single_arm": {"min": [-10.0] * 5, "max": [10.0] * 5},
},
},
modality_config={
"state": {"modality_keys": ["single_arm", "gripper"]},
"action": {
"modality_keys": ["single_arm", "gripper"],
"action_configs": [
{"rep": "RELATIVE", "type": "NON_EEF", "format": "DEFAULT", "state_key": None},
{"rep": "ABSOLUTE", "type": "NON_EEF", "format": "DEFAULT", "state_key": None},
],
"delta_indices": [0, 1],
},
},
)
transition = {
TransitionKey.OBSERVATION: {
OBS_STATE: torch.tensor([[10.0, 20.0, 30.0, 40.0, 50.0, 25.0]]),
},
TransitionKey.ACTION: torch.tensor(
[
[
[12.0, 18.0, 35.0, 30.0, 55.0, 0.0],
[9.0, 21.0, 27.0, 43.0, 50.0, 100.0],
]
]
),
TransitionKey.COMPLEMENTARY_DATA: {"task": ["Move"]},
}
output = step(transition)
expected_actions = torch.tensor(
[
[
[0.2, -0.2, 0.5, -1.0, 0.5, -1.0],
[-0.1, 0.1, -0.3, 0.3, 0.0, 1.0],
]
]
)
torch.testing.assert_close(output[TransitionKey.ACTION], expected_actions)
def test_groot_n1_7_pack_inputs_adds_inference_action_horizon_mask():
step = GrootN17PackInputsStep(
action_horizon=40,
@@ -1323,7 +1393,7 @@ def test_groot_from_pretrained_rejects_caller_config_mismatch_from_local_config(
# so construction itself raises before from_pretrained is reached.
with pytest.raises(ValueError, match="does not match base_model_path"):
config = GrootConfig(
base_model_path="nvidia/GR00T-N1.5-3B",
base_model_path="nvidia/GR00T-N1.5-3B",
input_features=input_features,
output_features=output_features,
device="cpu",
@@ -1736,9 +1806,7 @@ def test_groot_n1_7_saved_processors_reload_through_factory_preserves_saved_stat
assert unpack_step.env_action_dim == 7
def test_groot_n1_7_relative_action_training_processors_save_relative_action_stats(tmp_path):
def test_groot_n1_7_relative_action_training_processors_save_native_grouped_stats(tmp_path):
input_features, output_features = _groot_features(state_dim=6, action_dim=6)
action_names = [
"shoulder_pan.pos",
@@ -1817,26 +1885,41 @@ def test_groot_n1_7_relative_action_training_processors_save_relative_action_sta
postprocessor.save_pretrained(tmp_path)
preprocessor_config = json.loads((tmp_path / "policy_preprocessor.json").read_text())
assert any(step.get("registry_name") == "relative_actions_processor" for step in preprocessor_config["steps"])
assert not any(
step.get("registry_name") == "relative_actions_processor" for step in preprocessor_config["steps"]
)
pack_entry = next(
step
for step in preprocessor_config["steps"]
if step.get("registry_name") == "groot_n1_7_pack_inputs_v1"
)
pack_config = pack_entry["config"]
assert pack_config["modality_config"]["action"]["modality_keys"] == ["single_arm", "gripper"]
assert pack_config["modality_config"]["action"]["action_configs"] == [
{"rep": "RELATIVE", "type": "NON_EEF", "format": "DEFAULT", "state_key": None},
{"rep": "ABSOLUTE", "type": "NON_EEF", "format": "DEFAULT", "state_key": None},
]
assert pack_config["raw_stats"]["relative_action"]["single_arm"]["min"] == [-2.0, -3.0, -4.0, -5.0, -6.0]
assert pack_config["raw_stats"]["action"]["gripper"]["min"] == [0.0]
assert pack_config["raw_stats"]["action"]["gripper"]["max"] == [100.0]
pack_state = load_file(tmp_path / pack_entry["state_file"])
torch.testing.assert_close(pack_state[f"{ACTION}.min"], expected_relative_action_stats["min"])
torch.testing.assert_close(pack_state[f"{ACTION}.max"], expected_relative_action_stats["max"])
postprocessor_config = json.loads((tmp_path / "policy_postprocessor.json").read_text())
assert any(step.get("registry_name") == "absolute_actions_processor" for step in postprocessor_config["steps"])
unpack_entry = next(
assert not any(
step.get("registry_name") == "absolute_actions_processor" for step in postprocessor_config["steps"]
)
decode_entry = next(
step
for step in postprocessor_config["steps"]
if step.get("registry_name", "").startswith("groot_action_unpack_unnormalize")
if step.get("registry_name") == "groot_n1_7_action_decode_v1"
)
unpack_state = load_file(tmp_path / unpack_entry["state_file"])
torch.testing.assert_close(unpack_state[f"{ACTION}.min"], expected_relative_action_stats["min"])
torch.testing.assert_close(unpack_state[f"{ACTION}.max"], expected_relative_action_stats["max"])
decode_config = decode_entry["config"]
assert decode_config["use_relative_action"] is True
assert decode_config["raw_stats"]["relative_action"]["single_arm"]["max"] == [2.0, 3.0, 4.0, 5.0, 6.0]
assert decode_config["raw_stats"]["action"]["gripper"]["max"] == [100.0]
def test_groot_policy_selects_n1_7_model_class(monkeypatch):